Screening for amyotrophic lateral sclerosis through interactions with an internet search engine

Amyotrophic lateral sclerosis (ALS), a motor neuron disease, remains a clinical diagnosis with a diagnostic delay of over a year. Here we examine the possibility that interactions with an internet search engine could be used to help screen for ALS. We identified 285 anonymous Bing users whose queries indicated that they had been diagnosed with ALS and matched them to 1) 3276 control users and 2) 1814 users whose searches indicated they had ALS disease mimics. We tested whether the ALS group could be distinguished from controls and disease mimics based on search engine query data. Finally, we conducted a prospective validation from participants who provided access to their Bing search data. The model distinguished between the ALS group and controls with an area under the curve (AUC) of 0.81. Model scores for the ALS group differed from the disease mimics group (ranksum test, P<0.05 with Bonferrini correction). Mild cognitive impairment could not be distinguished from ALS (NS). In the prospective analysis, the model reached an AUC of 0.74. These results suggest that interactions with search engines could be used as a tool to assist in screening for ALS, to reduce diagnostic delay.